Online battery state of power prediction using PRBS and extended Kalman filter

This paper presents a hybrid battery parametrisation technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centred around an opportunistic initialisation of a dual Extended Kalman Filter (DEKF) algorithm using Pseudo Random Binary Sequence (PRBS) battery excitation. A Second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilised to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC and subsequently its SOP in real time. The experimental results obtained form an electrochemical impedance spectroscopy (EIS) method give confidence to the performance of the proposed hybrid battery parametrisation technique

State of charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium‐ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time‐varying temperature from −20°C to 60°C. Then, the genetic algorithm is leveraged to identify the temperature‐dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time‐varying temperature range with the maximum error of less than 2%

An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack

Online reduced complexity parameter estimation technique for equivalent circuit model of lithium-ion battery

For control-oriented battery management applications in electric vehicles, Equivalent Circuit Model (ECM) of battery packs offer acceptable modelling accuracy and simple mathematical equations for including the cell parameters. However, in real-time applications, circuit parameters continuously changes by varying operating conditions and state of the battery and thus, require an online parameter estimator. The estimator must update the battery parameters with less computational complexity suitable for real-time processing. This paper presents a novel Online Reduced Complexity (ORC) technique for the online parameter estimation of the ECM. The proposed technique provides significantly less complexity (hence estimation time) compared to the existing technique, but without compromising the accuracy. We use Trust Region Optimization (TRO) based Least Square (LS) method as an updating algorithm in the proposed technique and validate our results experimentally using Nissan Leaf (pouch) cells and with the help of standard vehicular testing cycles, i.e. the Dynamic Driving Cycle (DDC), and the New European Driving Cycle (NEDC)

Stage of Charge Estimation of Lithium-ion Battery Packs Based on Improved Cubature Kalman Filter with Long Short-Term Memory Model

Accurate estimation of state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, firstly, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate battery cell’s SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation value from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited within 2% after convergence

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation

An improved packing equivalent circuit modeling method with the cell-to-cell consistency state evaluation of the internal connected lithium-ion batteries.

The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium-ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium-ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real-time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real-time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance-capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium-ion battery pack in the power supply application

A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization.

The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is less than 0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs